Patients with rare diseases are typically left misdiagnosed or undiagnosed, which leads to a prolonged medical journey. In our previous study, we applied a collaborative filtering model on clinical data generated at Mayo Clinic to stratify patients into subgroups of rare diseases based on their phenotypic characterizations. Information mined from clinical data, however, usually contains a certain level of noise. In this study, therefore, we sought to incorporate a knowledge-driven approach into collaborative filtering to optimize results learned from clinical data.
Learning Objective 1: From this study, we combined knowledge and data driven insights to facilitate the differential diagnosis of rare diseases
Feichen Shen (Presenter)
Hongfang Liu, Mayo Clinic